Each year, roughly 18,000 Americans will be found to have malignant glioma brain tumors and approximately 13,000 will die. Despite advances in surgery, radiation therapy, and chemotherapy, median survival for the most aggressive forms of the disease such as glioblastoma have remained at 12 months over the past decade. Faced with growing evidence that these traditional therapies have failed to improve the clinical course of this lethal disease, researchers are now turning to novel approaches including the identification of genes that facilitate tumor invasiveness and mobility. By targeting these genes that lead to aberrant activation of mitogenic signaling and cell cycle control, it is hoped that ne treatments can be developed to address these devastating diseases. Our previous research from 13 independent microarray studies has identified 180 genes (92 up regulated and 88 down regulated) that are significantly altered in glioblastoma patients. By utilizing a Bayesian network learning method, we were able to identify the requisite state (whether each of the genes is expressed high or low) of the following genes that is required to identify whether subjects have glioblastoma: DPYSL3, NUP205, C1S, MEF2C, LDOC1, FOXO4, and SPOCK3. Based on these data, we hypothesize that a minimum of six to eight key novel genes are involved in the causation of aggressive brain cancer (in this proposal we refer aggressive brain cancer as malignant grades of gliomas, i.e., grade II astrocytoma, grade III astrocytoma, and glioblastoma (grade IV astrocytoma)). The pay-off will be substantial if we are able to identify key genes in the progression of malignant grades of gliomas because this will help health professionals to provide a better treatment for patients with aggressive brain cancer and improve their prognosis. We have identified the following specific aims to test our hypothesis: Aim 1: Identify key genes and their overall pathway and transcription factors in the progression of aggressive brain cancer. Aim 2: Identify better treatments for patients with aggressive brain cancer and new gene targets for therapy of aggressive brain cancer using a causal Bayesian statistical model. We hypothesize that the causal learning system will discover novel causal relationships among genes, treatment, and survival. If so, the system will provide a better treatment for patients with aggressive brain cancer and improve their prognosis.